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Gesis.jobs.personio.de

Student Research Assistant, Team Transparent Social Analytics (SHK-CSS-40)

  • Seen live on Gesis.jobs.personio.de's site today
  • Intern / co-op
Pay
Not stated
Location
Köln
Open for
7 days
First seen
Oct 28, 2026
Apply on Gesis.jobs.personio.de's site (opens in a new tab)

Posting record

No flags

Open 7 days

The posting's record, oldest first
  1. Gesis.jobs.personio.de's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age.

Last checked Nov 5, 2026.

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What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

Â

GESIS – Leibniz-Institute for the Social Sciences is an internationally active research institute, funded by federal and state governments and member of the Leibniz Association.

Â

Starting as soon as possible, our Department Computational Social Science (CSS) , Team Transparent Social Analytics located in Cologne , is looking for a

Â

Student Research Assistant

(15,20 € / 16,09 € hourly rate, 10 hrs./week, temporary)

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The department Computational Social Science (CSS) collects digital behavioral data and provides computer-based methods for collecting and analyzing such data for social science research. It also supports scientists with the integration of digital behavioral data into their research designs. The department's research focuses on the quality of digital behavioral data, the development and validation of computational social science methods and the transformation of digital societies. The Transparent Social Analytics team specifically works on making methods for the analysis of digital behavioral data transparent and accessible, among other things as part of the research-supporting infrastructure GESIS Methods Hub .

Your tasks will be

  • Work in an interdisciplinary team developing innovative Open Science services for Digital Behavioral Data
  • Support the documentation and provision of Computational Social Science methodsÂ
  • Contribute to AI-enabled social science research infrastructure, including agentic workflows and AI-features
  • Review current research literature at the intersection of Data Science, AI, and the social sciences

Your profile

  • Enrolled in an advanced Bachelor’s or Master’s programme in Sociology, Political Science, Communication Studies, Linguistics, Computer Science, Data Science, or a related field
  • Strong knowledge of Python and/or R, with a willingness to develop foundational skills in the other language
  • Interest in addressing social science research questions using computational methods (Computational Social Science)
  • First experiences in applying Data Science and AI methods to analyse digital behavioral data (e.g., social media or tracking data) for social-science researchÂ
  • Excellent English language skills; proficiency in German is an advantage

Our Benefits

  • An interdisciplinary and international working environment with a friendly and collegial atmosphere, offering plenty of opportunities for professional and methodological development.
  • Exciting insights into the strategic and technical development of an innovative service designed to support social science research
  • Excellent conditions for balancing your studies with your work as a student assistant
  • Flexible working hours and regulations for mobile working
  • Holistic company health management and discounted participation in the university's sports programmeÂ
  • Promotion of your skills through further training measures

Contact

For further information concerning the tasks please contact Christina Viehmann via E-Mail . If you have questions about the application process, please contact Christine Laßmann via E-Mail .

Interested?

Please apply via our Online-application portal until  30.11.2026 .

Our reference number is: SHK-CSS-40

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